Best Machine Learning Courses & Training of 2020

Our experts have curated a list of the 12 best online courses for machine learning currently available on the internet.

These machine learning courses are taught by industry experts and pioneers, including former Google engineers. They will help you get started on your path to learn machine learning which is quickly becoming one of the foremost and fascinating in the computer field.

Businesses are constantly coming up with new ways to leverage machine learning. These courses offer novice to advanced students a way to gain valuable knowledge in the field of artificial intelligence.

Best Machine Learning Courses and Certifications

This course offers an in-depth look at machine learning and the instructors show and explain the workings of both Python and R. Each concept is explained thoroughly which is easy to follow and understand.

The instructors have made this course both highly informative and fun to learn. They have also provided detailed step by step information to install the required software and tools.

In this course the instructors cover all about data processing, regression, classification, and clustering. You will also learn to make your own machine learning models. This course covers fascinating topics like the Convolutional Neural Networks, Multi-Armed Bandit problem and Grid Search algorithm.

This is one of the highly recommended machine learning courses if you want to get hands-on experience of implementing machine algorithms with clear cut understanding. This course includes 293 lectures which take a time period of 41 hours.

The courses by Kirill Eremenko and Hadelin de Ponteves are considered some of the best on Udemy. It has over 425,655 enrollments and has a rating of 4.5 out of 5.

Kirill is a data scientist who has years of experience in AI. He is one of the top instructors and offers multiple courses on the Udemy platform. He is passionate about tutoring, making even the complex concepts easier.

Hadelin is an expert in machine learning and Artificial Intelligence, and has a Master’s degree with a specialization in Data Science. He is a tech enthusiast who spends most of his time learning and teaching courses on different scientific topics.

Who is this course for?

It is an excellent choice for you if you are a beginner interested in machine learning. Basic high school mathematics is recommended for you to take up this course..

Highlights of the course

Exposing you to more complex topics like Natural language processing (NLU)

The course is offered by Stanford University and Andrew Ng, co-founder of Coursera and Adjunct Professor of Computer Science at Stanford University designed it.

Andrew has a passion for machine learning and led the “Google Brain” project, which developed massive-scale deep learning algorithms. He also led Baidu’s AI Group, which developed various technologies in deep learning, speech, computer vision, and NLP.

In this course, Andrew begins with an introduction to Machine Learning, data mining, and statistical pattern recognition. He shows the use of Matlab and explains each algorithm and the math behind it in a clear and concise manner.

In this course, you will learn the most effective knowledge engineering techniques and implement the best practices to improve your skills. You will have a strong foundation in the basics of deep learning and its application in the real world.

This course is comprised of 11 modules which approximately take around 55 hours to complete. This highly rated course is strongly recommended to strengthen your basics in algorithms. By the end of this course, you would have learned to build smart robots, text understanding, computer vision, medical informatics, audio and database mining.

Most of the students enrolled have benefited tremendously from this course. Stats shows that around 40% of the students started a new career after completing this course and 38% of the students got a career benefit from this course.

Who is this course for?

This course is for absolute beginners who want to know the basics and workings of algorithms, computational statistics, and AI. If you are a beginner and want a thorough understand of deep learning and how it will be used in the future, this course is a sold starting point.

Highlights of the course

Content taught by experts

Assignments are well designed and help you write algorithms and statisical models with no prior experience

Pluralsight is an online skills platform which helps developers and IT professionals enhance their skills. This course by David Chappell is one of the highly rated courses on Pluralsight. David is an excellent tutor who has a great passion for teaching. He has published several books in various languages and is one of the top instructors on Pluralsight.

In one of the more beginner-friendly courses, David explains the basic fundamentals of Machine Learning. He begins with ‘What is Machine Learning?’ and moves further to topics like its workings and process.

By the end of this course, you will have a strong foundation on the basics of machine learning and have a deep understanding of training and testing a model. This course includes three modules spanning over a time period of 37 minutes.

Who is this course for?

If you want to get started in algorithms and learning neural networks and have no prior experience, this course is an ideal choice for you. This introductory class will help you solidify the basic concepts which is easily understandable and prep you to the more complex topics.

Highlights of the Course

This course is another best seller from Udemy by Jose Marcial Portilla. Jose is an experienced professional who has a passion for teaching Data science and programming. In this course, almost everything is explained in a fundamental and detailed way which makes it easily understandable. Jose is an excellent instructor who explains each concept and examples step by step in a clear and concise manner.

This course introduces you to deep learning approaches using the most popular library, TensorFlow. Jose gives a recap of the Math and Machine Learning concepts for each section. It covers the basics and workings of neural networks and TensorFlow. You will also learn to use TensorFlow for Classification and Regression Tasks.

Apart from that, Jose explains about the popular models such as AutoEncoders, Reinforcement Learning, and GAN. At the end of this course, you will have enough depth to start playing with the TensorFlow library. You will have a lot of insight and practical understanding of algorithms, Deep Learning and TensorFlow using Python.

This is a comprehensive course which is well structured and well presented. Jose makes sure each concept is very informative and provides challenging assignments that will help you apply the concepts in practice.

Around 61,678 students have enrolled for this course which has an average rating of 4.5 out of 5. This is one of the highly recommended courses and is comprised of 96 lectures spanning over a time period of 14 hours.

Who is this course for?

This course is an ideal choice for you if you are a beginner who wants to gain a deep understanding of the TensorFlow framework and neural networks.

Before enrolling in this course, it is recommended that you have prior knowledge in programming, preferably Python, and have familiarity with basic high school math. If you are a Python developer who wants to learn the latest Deep Learning Techniques with TensorFlow, then this is a perfect course for you to get started with.

Codecademy is one of the pioneers in online education platforms. It offers a plethora of courses for developers and IT professional who want to enhance their skills. This Machine Learning course content spans over a period of 15 hours and includes over 11 modules. This course has over 75,219 students and is one of the highly recommended courses to get started in AI.

This course begins with “What is Machine Learning” and explains the basics of it. The instructors explain each concept through programming by using real-world datasets.

The is no math prerequisites, as the instructors will guide you through the math concepts and explain them in a concise manner which is easily understandable.

This course covers topics like regression, classification, clustering, and perceptron. Apart from that, you will learn about Minimax in Artificial Intelligence and its advanced concepts.

At the end of each module, you will either have a free form project or quizzes to help you familiarize yourself with the concepts learned. This course is highly beneficial for beginners who want to have a deep understanding of the workings of algorithms and neural networks. After completion you will have enough knowledge to build your own projects.

Who is this course for?

If you are a beginner and want to build a strong foundation in Machine Learning, this course is perfect for you. It covers all the foundational algorithms that will help you advance your career. This course is an ideal choice for intermediate learners who want to be exposed to more advanced topics.

It is the best place to start if you are a data analyst who is looking to upgrade your skills or a programmer who is trying to analyze a dataset using machine learning. It is recommended that you must be familiar with Python, including functions, control flow, lists, and loops.

Highlights of the course

Learn to build different ML algorithms from scratch before implementing them with scikit-learn

This is a beginner level course which has 7 modules and spans over a time period of 1 hour and 4 minutes. The course instructor provides you with a step by step guide to set up the development environment.

This course covers the basics of machine learning and exposes you to build your own learning system. Adam also shows how to work with large sets of data efficiently.

In this course, you will learn to measure accuracy with mean absolute error and make predictions using the machine learning model. Here, the project mainly focuses on real estate, whereby by the end of the course you can use the same approach to solve any kind of value estimation problem with machine learning and have a grasp on gradient boosting algorithm scikit-learn.

This comprehensive course has over 47,565 participants. Adam explains each concept in the right way which is clearly understandable. Finally, with all the basic concepts covered, you will be able to apply the logic to any machine learning library no matter what programming language you use.

Adam Geitgey is a well-known software developer who is captivated by AI and its impact on software development. He is one of the top instructors on LinkedIn Learning.

He has a Bachelor’s in Computer Science and has over 15+ years of experience. Being an expert, he helps start-ups build and enhance their organization with the help of algorithms and “thinking” computers.

Who is this course for?

This a beginner level course to learn the basics of Machine Learning and AI foundations. If you are looking for a course to start learning the basics and the program on value estimations, this course is a perfect fit for you. Before enrolling in this course, you should be familiar with programming. It is recommended that you have some basic knowledge in Python for easy understanding, but it is not mandatory.

edX is one of the online course providers for gaining new skills. It offers courses from the best universities and industries. This course was offered by IBM and taught by Saeed Aghabozorgi who is currently a Sr. Solutions Architect (ML/AI Specialist) at Amazon Web Services (AWS).

He is a specialist in machine learning and has over 10+ years of experience. Saeed has a Ph.D. in machine learning and is a highly skilled professional who is into Deep Learning and building solutions to leverage artificial intelligence approaches.

In this course, Saeed explains the basics of machine learning using Python. This course covers Supervised, Unsupervised Learning and the ways this learning impacts society. The instructor also shows how Statistical Modelling relates to artificial intelligence.

You will be introduced to many popular algorithms like Classification, Regression, Clustering, and Dimensional Reduction. Apart from that, this course also covers popular models such as Train/Test Split, Root Mean Squared Error and Random Forests.

This is a pretty impressive course which has detailed explanations with real-time examples. You will also learn to apply your theoretical knowledge practically as there is a hands-on lab offered to improve your skills. This course has 5 modules which can be completed within 5 weeks if you put in 4 to 6 hours of effort per week.

Who is this course for?

This is an introductory class which is perfect for you if you are a unfamiliar with machine learning and want to start from scratch. But like many other courses, it is required that you have prior knowledge in the basics of Python and data science.

Want to be a Data Scientist or Data analyst? This course will be helpful to kick start your career in machine learning.

Highlights of the course

Content taught by an experienced professional

Offers practical assignments and quizzes that help you understand each concept better

This specialization in Coursera is offered by the University of Washington which is one of the preeminent research universities in the world. This course was taught by Emily Fox and Carlos Guestrin who are Amazon Professors of machine learning. Every specialization in Coursera consists of a hands-on project and a course completion certificate.

This specialization consists of four courses. The instructors begin with the basic fundamentals of Machine Learning concepts. You will also learn to implement learning models and train data. Concepts like Regression, Classification, Recommender, Clustering and Deep Learning are well understood by applying these concepts in the tasks provided such as Predicting House Price, Google image search, Image-based filtering, etc.

In the second course, the instructors explain regularized linear regression. Here, the instructors show how to handle very large sets of features and select between models of various complexities. The course dives further into utilizing machine learning with regression-based methods which also uses Python. You will learn to write the algorithms for OLS regressions, ridge regression, lasso regression, and for k-nearest neighbor models.

The next course has an amazing explanation of anything related to classification. In this course, you will learn about the best practices and implementing tasks similar to real-world applications. Apart from that, the instructors show the techniques to handle missing data and evaluate your models using precision-recall metrics. You will also learn to implement the techniques you learned in Python.

Finally, the last course is all about Clustering & Retrieval. This course is more challenging and has many advanced concepts and real-life implementations. You will learn about structured representations for describing the documents in the corpus such as latent Dirichlet allocation (LDA). The course also covers the implementation procedure on expectation maximization (EM) to learn about document clustering.

The course is well structured and the assignments at the end of each module solidify the understanding of the concepts well. The lecturer is very good, and the information is very comprehensive. This course is one of the highly rated courses which has an average rating of 4.8 out of 5. More than 60,000+ students have enrolled for this course which will take approximately 6 months to complete.

Students who completed this course have benefited tremendously. Stats show that 40% of the students enrolled in this specialization started a new career, 42% of the professionals got career benefits from the course and 20% of the learners got promoted or a raise in pay.

Who is this course for?

This course is for beginners who want to start from scratch and move their way to more advanced topics in Machine Learning. If you are an intermediate learner or advanced professional, this course is still helpful in enhancing your skills to become an expert in Machine Learning and have a clear understanding of the fundamentals.

This course by Doug Rose is one of the highly recommended courses on LinkedIn learning. Doug has 10+ years of experience and specializes in organizational coaching, training and change management. This course is comprised of 4 modules which span over a time period of 1 hour and 17 minutes. Around 49,633 learners have joined in the course.

Doug begins with the definition of Machine Learning and explains the types of ML such as supervised, unsupervised, and reinforcement. This is a beginner level course that is comprised of video course content regarding current concepts and technologies. Besides that, you will also learn about the pitfalls when starting out with Machine Learning and the common challenges faced during the development of neural networks.

By the end of this course, you will be familiar with how to work with data, create decision trees and to select the best algorithm. At the end of each module, you will be provided with lots of quizzes to help you improve your skills.

Doug is an exceptional tutor who has structured the course pretty well. This course is easily understandable for anyone and doesn’t have any prerequisites.

Who is this course for?

This course is for beginners who want to have a better understanding of machine learning and its basic concepts. If you are a novice wanting to learn the fundamentals quickly and easily, this course is the right choice for you.

This course is not only for data scientists but also for students, developers and managers who want to have a better understanding of core concepts.

Highlights of the course

This is another successful course on Udemy by Frank Kane. He is one of the top instructors on Udemy and has 9+ years of experience. His way of teaching is simple and its always step by step, which makes it easy to understand.

In this course, you will learn the basics of data science, Machine Learning and deep learning. Frank also shows you how to use Python for Machine Learning. It covers everything that you need to to start working in data science.

Frank has well-prepared materials and his delivery is clear and concise. He explains each concept with perfect examples and provides lots of practice data with the code to execute in Python. This course covers Neural Networks (MLP’s, CNN’s, RNN’s) with TensorFlow and Keras. Furthermore, this course also includes clustering, regressions, K nearest neighbor and Ensemble learning.

This course covers a wide range of information about machine learning, data science, and deep learning. Frank made sure it is easily understandable even for a beginner and provides examples that are applicable for real-world problems for both novice and experienced professionals.

At the end of this course, you will learn to design and evaluate A/B tests using T-Tests and P-Values and will also have a better understanding in reinforcement learning – and how to build a Pac-Man bot. This course is comprised of 101 lectures spanning over a time period of 13 hours and 5 minutes. It is one of the best sellers in Udemy with a rating of 4.5 out of 5 and has over 95,449 students.

Who is this course for?

This course is definitely for you if you want to understand more about data science. If you are looking to quickly learn about Data Science, machine learning, and neural networks this course is highly recommended. It is required that you have prior knowledge in coding or be familiar with any programming languages. High school level math skills is also required.

If you are a programmer looking for a new career path or a data analyst interested in enhancing your tech skills, this course is absolutely the right choice for you.

This course is offered by one of the top universities in the world. Rafael Irizarry, a professor from Harvard University structured this course on the edX platform. Rafael is a Professor of Biostatistics at Harvard University. He has a Ph.D. in Statistics and a Bachelor’s in Mathematics. He has developed several online courses on data analysis which were completed by lots of students.

In this course, you will learn about probability, inference, regression, and Machine Learning. The instructor also shows the basics and workings of R programming.

This course also includes motivating case studies like Trends in World Health and Economics, US Crime Rates, The Financial Crisis of 2007-2008, Election Forecasting, building a Baseball Team and Movie Recommendation Systems which makes the learning more fun. In this course, you will be exposed to more popular packages from R programming like tidyverse, including data visualization with ggplot2 and data wrangling with dplyr.

You will learn statistical concepts such as probability, inference, and modeling and apply them in practice. Besides that, the instructor also shows how to implement learning algorithms.

By the end of this course, you will be familiar with tools like Unix/Linux, GitHub, and RStudio. You will also have a deep understanding of fundamental data science concepts through real-world case studies.

This course consists of 9 modules which can be completed within 2 to 3 months’ time. The course is well designed and very informative with real life case studies.

Who is this course for?

Demand for Data scientist and Data Analyst is increasing day by day. If you want to carve your career in Data Science, this course is absolutely the right choice for you as it covers the fundamentals of R programming and dives into Machine Learning.

Yet another best-selling course on Udemy by Lazy Programmer Inc. The instructor is an Artificial intelligence and Machine Learning engineer. He has a master’s in computer engineering with a specialization in machine learning models and pattern recognition.

The instructor begins with the outline of the Hidden Markov Model and explains the mathematics behind Markov chains. You will also understand the workings of the Markov model where the instructor shows how to apply the Markov model to any sequence of data.

This course also covers the popular libraries in deep learning such as Theano and TensorFlow which is useful in learning neural networks and LSTMs. In this course, you will learn about HMMs for stock price analysis, language modelling, web analytics, and biology. Moreover, the instructor also shows how Google uses pay rank and explains how it can be used in Natural language processing.

It is an in-depth and comprehensive curriculum which has over 15,312 students enrolled. This course is comprised of 60 lectures with a time period of 8 hours and 30 minutes.

Who is this course for?

If you are familiar with the fundamentals of artificial intelligence and machine learning models and want to further enhance your skills, this course is a perfect fit for you. But before enrolling in this course, you must be familiar with probability and statistics. You must also have a good understanding of Gaussian mixture models and have knowledge of Python and Numpy.

If you’re a professional looking to work on data analysis especially sequence data, this course is absolutely the right choice for you as the instructor explains everything in a simple manner.

Why Learn Machine Learning?

The business world is evolving with large amounts of data produced every day. The data generated helps in making better decisions for the business.

To reduce these mundane tasks and make them simpler, the machines are programmed to perform specific tasks by building an iterative model. The training of the machine involves learning and re-learning for the previous computations to produce reliable and better results.

So, do machines have the ability to think? Absolutely not! They work on what they are programmed to do. They learn from past mistakes, look for certain patterns to build the model and improve their efficiency.

According to the Gartner report, demand for Artificial Intelligence professionals will increase by 38% by 2020.

While Artificial Intelligence is blooming up, machine learning is a subset of AI that trains the system to analyse as programmed. It is an area of computation science that involves analyzing the data and interpreting the patterns made outside the construct of human interaction.

Data-driven decisions helps businesses to stand ahead of the competition. Predicting the future with the historical data helps companies to build the strategies well ahead of time. Machines assist in filtering important information by developing testing algorithms involving mathematical calculations for better problem solving.

Machine Learning is slowly creeping to the top as the demand for data scientists is booming day by day. The impact of machine learning models in software development is also increasing. Therefore, developers and IT professionals are sharpening their skills not to be left behind.

These are our pick for the 12 best online courses to learn machine learning theory, techniques, and business applications. This list is for individuals who are aiming to carve their path in machine learning or professionals who want to enhance their skills for career advancement.